Artificial intelligence article analysis interface

ABSTRACT

Technologies for an artificial intelligence article analysis interface are described. The interface is configured to render one or more metrics associated with articles written using artificial intelligence. In some examples, the interface data is provided by an article analyzer. The article analyzer may analyze one or more articles and provide one or more outputs. The article analyzer may receive as an input one or more articles provided by an artificial intelligence journalist or human journalist.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.14/827,011 filed Aug. 14, 2015, which claims the priority of U.S.Provisional Patent Application No. 62/193,189, filed Jul. 16, 2015, ofwhich the entire disclosure and contents of these are herebyincorporated by reference herein.

BACKGROUND

Internet news services are becoming increasingly common. Internet newsservices may act as news sources or news aggregators. Generally, a newssource is an individual or organization that observes events andgenerates news articles about the event. A news aggregator may collectone or more news articles provided by one or more news source andprovided the collected news articles to a reader. The two types of newsservices are not mutually exclusive. In some contexts, a news source mayalso act as a news aggregator, and a news aggregator may act as a newssource.

It is with respect to these and other considerations that the disclosuremade herein is presented.

SUMMARY

The following detailed description is directed to technologies for anartificial intelligence article analysis interface. The interface isconfigured to render one or more metrics associated with articleswritten using artificial intelligence. In some examples, the interfacedata is provided by an article analyzer. The article analyzer mayanalyze one or more news stories and provide one or more outputs. Thearticle analyzer may receive as an input one or more articles providedby an artificial intelligence journalist or human journalist.

The artificial intelligence journalist may access one or more datasources providing information about an event. The artificialintelligence journalist may apply an algorithm to draft a news articlefrom the one or more data sources. The article analyzer may analyze thearticles drafted by the artificial intelligence journalist as well asarticles drafted by a human and determine one or more metrics oroutputs.

In some examples, the artificial intelligence article analysis interfacemay be used as a search-based application. For example, the interfacemay be used to find and retrieve articles created by artificialintelligence. Keywords associated with the articles may be used assearchable terms to identify articles relating to a particular searchquery.

It should be appreciated that the subject matter described herein may beimplemented as a computer-implemented method, computer-controlledapparatus, a computer process, a computing system, or as an article ofmanufacture such as a computer-readable medium. These and various otherfeatures will be apparent from a reading of the following DetailedDescription and a review of the associated drawings.

This Summary is provided to introduce a selection of technologies in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intendedthat this Summary be used to limit the scope of the claimed subjectmatter. Furthermore, the claimed subject matter is not limited toimplementations that solve any or all disadvantages noted in any part ofthis disclosure

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a system diagram showing one illustrative operatingenvironment that may be used to implement various embodiments describedherein.

FIG. 2 is a diagram showing an application dashboard for displayingmetrics provided by an article analyzer.

FIG. 3 is a diagram showing an application dashboard when using a searchfunction.

FIG. 4 is a diagram showing an application dashboard rendering a newdiscovery index.

FIG. 5 is a diagram showing an application dashboard rendering a topstories and trending section.

FIG. 6 is a diagram showing an application dashboard rendering a topstories and trending section having an articles by region section.

FIG. 7 is a diagram showing an application dashboard rendering a customtopics section.

FIG. 8 is a diagram showing an application dashboard by which ananalysis is conducted.

FIG. 9 is a diagram showing an application dashboard after a search isperformed by a server computer in conjunction with an analysis function.

FIG. 10A is a diagram showing an application dashboard after variousfilters and other inputs are applied to search results.

FIG. 10B is a method for generating and applying a keyword.

FIG. 11 is a diagram showing an application dashboard after the variousfilters and other inputs are applied to search results in a microanalysis step.

FIG. 12 illustrates an example of a visualization that may be providedfor the search results.

FIG. 13 is a diagram showing an application dashboard having renderedtherein a download interface.

FIG. 14 is a diagram showing an application dashboard in a configurationto provide a visualization.

FIG. 15 is a diagram showing an application dashboard havingvisualizations provided by an article visualizer.

FIG. 16 is a flow diagram showing aspects of a method for generating anartificial intelligence article.

FIG. 17 is a flow diagram showing aspects of a method for analyzing datausing artificial intelligence articles.

FIG. 18 illustrates an illustrative computer architecture for a devicecapable of executing the software and/or hardware components describedherein for an artificial intelligence article analysis interface.

FIG. 19 illustrates an illustrative distributed computing environmentcapable of executing the software components described herein for anartificial intelligence article analysis interface.

DETAILED DESCRIPTION

Embodiments of the disclosure presented herein encompass technologiesfor an artificial intelligence article analysis interface. In thefollowing detailed description, references are made to the accompanyingdrawings that form a part hereof, and in which are shown by way ofillustration specific embodiments or examples. Referring now to thedrawings, aspects of an example operating environment and some exampleimplementations provided herein will be described.

FIG. 1 is system diagram showing one illustrative operating environment100 that may be used to implement various embodiments described herein.The operating environment 100 may include a user device 102 and a servercomputer 104. The user device 102 and/or the server computer 104 are notlimited to any particular type or configuration of computing platform.

The user device 102 and/or the server computer 104 may be one or morecomputing devices that, when implemented together, may be used as a userdevice 102 and/or a server computer 104. The user device 102 and/or theserver computer 104 may be implemented in various forms, including, butnot limited to, a mobile device, a cell phone, a tablet computer, adesktop computer, a laptop computer, and the like. The presentlydisclosed subject matter is not limited to any particularimplementation. In some implementations of the presently disclosedsubject matter, all or some of the features may be implemented on eitherthe user device 102 and/or the server computer 104.

The user device 102 may be placed in communication with the servercomputer 104 using a network 106. FIG. 1 illustrates one user device102, one network 106, and one server computer 104. It should beunderstood, however, that some implementations of the operatingenvironment 100 include multiple user devices 102, multiple networks106, and/or multiple server computers 104. The illustrated examplesdescribed above and shown in FIG. 1 should be understood as beingillustrative, and should not be construed as being limiting in any way.It should be understood that the concepts and technologies disclosedherein are not limited to an operating environment 100 connected to anetwork or any external computing system, as various embodiments of theconcepts and technologies disclosed herein can be implemented locally onthe user device 102 and/or the server computer 104.

The user device 102 may be configured to initiate and execute ananalyzer interface application 108. In some examples, the analyzerinterface application 108 may be an application configured to receiveone or more inputs and render the inputs in a user interface, examplesof which are provided below, on a display 110. The analyzer interfaceapplication 108 may receive one or more inputs from a user 112 tocontrol one or more features rendered by the analyzer interfaceapplication 108. In some examples, the analyzer interface application108 may be an Internet browser.

The analyzer interface application 108 may be configured to communicatewith an article analyzer 114 executed by the server computer 104. Thearticle analyzer 114 may be configured to access an articles data store116. The articles data store 116 is configured to store artificialintelligence (“AI”) articles 118 written by an artificial intelligencejournalist 120 and human articles 122 provided by human sources. As usedherein, an article is a piece of writing that refers to and discusses,at least in part, an event.

The presently disclosed subject matter is not limited to any particulartype or form of article, and may include, but is not limited to, a shortdescription of an event, a book, a report, narrative, analytics, and thelike. As used herein, an event includes an occurrence of something, andis not limited to any particular kind of event. For example, an eventmay be a sports contest, a financial occurrence, the disclosure ofresearch, and the like. The presently disclosed subject matter is notlimited to any particular type of event.

As used herein, artificial intelligence refers to intelligence providedby machines or computers. As used herein, the artificial intelligencejournalist 120 is a computer or process whereby a computer, such as theserver computer 104, applies algorithms to event information andgenerates the AI articles 118. In one example, event information may bereceived by the article analyzer 114 when searching and accessing datasources 124A-124N ((hereinafter referred to collectively and/orgenerically as “the data sources 124” and individually as “the datasource 124A,” and the like). The data sources 124 may include documentsor information provided by one or more sources that relate to an event.

In some examples, artificial intelligence journalist 120 may receive asinput event information about an event provided by the data sources 124.The artificial intelligence journalist 120 may apply one or more filtersto reduce or eliminate certain types of information. For example, theartificial intelligence journalist 120 may apply a filter to rawinformation that recognizes non-factual, opinion, colloquial,relativistic, or others types of information to create filteredinformation. The artificial intelligence journalist 120 may thereafteradd information to the filtered information to connect various conceptsfound in the filtered information to provide for enhanced information.The enhanced information may thereafter be stored as the AI article 118.

For example, the artificial intelligence journalist 120 may receive thefollowing snippet of an article from the data sources 124: Scientists inBelgium report that Mary has discovered a new element. Mary is fromSouth Carolina and enjoys surfing and racquetball. Mary has named theelement, Marium. We do not think that Mary is telling the truth andwould like to see the final outcome when published in Scientist Daily.

As can be seen in the article above, the article includes factualinformation, opinion information (We do not think . . . ), andinformation that is not relevant (Mary is from . . . ) to the main story( . . . discovered a new element). In some examples, the artificialintelligence journalist may apply one or more filters to create thefiltered information. In one example, the artificial intelligencejournalist may identify the particle event (or subject) of the article:the discovery of a new element.

The artificial intelligence journalist 120 may then analyze the articleto determine which words or sentences are most likely applicable to theevent and maintain those words or sentences while filtering out orremoving the words or sentences that are least likely applicable. Thefollowing may be the result of the filtering operation: Scientists inBelgium report that Mary has discovered a new element . . . Mary hasnamed the element, Marium . . . final outcome when published inScientist Daily.

As can be seen in the example provided above, while condensed toinformation relating to the event, if read by a human, the aboveinformation may not be easy or pleasant to read. The artificialintelligence journalist 120 may then apply information to createenhanced information, resulting in the AI articles 118.

Continuing with the example above, the enhanced article may be thefollowing (with additions shown underlined and subtractions shown withstrikethroughs only for purposes of description).

Scientists in Belgium report that Mary has discovered a new element.Mary has named the element, Marium. [The] final outcome will bepublished in Scientist Daily.

It should be noted that the presently disclosed subject matter is notlimited to the above-described algorithm, and may include othertechnologies for generating artificial intelligence articles. Forexample, a human or artificial intelligence generated article may bereceived. Using technologies, such as text mining, important concepts oritems within the article may be mined, the concepts of which may be theprimary topic of the article, the origin of research relating to thearticle, various introductory phrases, research methods and/or protocolsidentified in the article, results discussed in the article, or otheritems that are identified as concepts of greater importance than otherconcepts.

The user 112, or other entity, may request that the AI articles 118and/or the human articles 122, be analyzed to provide some metrics,examples of which are described in various figures below. The articleanalyzer 114 may invoke an article visualizer 126. The articlevisualizer 126 may receive one or more metrics generated by the articleanalyzer 114 and output one or more visualizations to be rendered in thedisplay 110. The user 112 may provide a user input 128 to select thevisualizations, described in more detail below.

FIG. 2 is a diagram showing an application dashboard 202 for displayingmetrics provided by inter alia the article analyzer 114 of FIG. 1. Theapplication dashboard 202 may be rendered within or in conjunction withthe analyzer interface application 108 of FIG. 1. In some examples, theanalyzer interface application 108 may be an Internet browser configuredto receive inputs from the user device 102 and communicate with one ormore remote servers, such as the server computer 104, through anInternet connection, such as one provided by the network 106. In otherexamples, the analyzer interface application 108 may be another type ofapplication capable of performing the functions described herein. Thepresently disclosed subject matter is not limited to any particular typeof application.

The application dashboard 202 may include a control section 204. Thecontrol section 204 may include one or more selectable controls. In theexample illustrated in FIG. 2, the one or more selectable controlsinclude a dashboard control 206A, a search control 206B, an analyzecontrol 206C, a visualize control 206D, a preferences control 206E, andan accounts control 206F (hereinafter referred to collectively and/orgenerically as “the controls 206” and individually as “the dashboardcontrol 206A,” and the like). The controls 206 may be configured toreceive an input and perform a certain function. For example, thedashboard control 206A is configured to receive an input and display theapplication dashboard 202. The preferences control 206E is configured toreceive a selection input to allow a user to determine variousconfigurations. The accounts control 206F is configured to receive aselection input to allow a user to access various user accountsassociated with the analyzer interface application 108. The othercontrols 206 are described in more detail below.

The application dashboard 202 may also include a total articlesindicator 208. The total articles indicator 208 may be a numericalcounter that indicates a total number of articles. The total number ofarticles may be the total number of AI articles 118, the total number ofhuman articles 122, or other totals as may be appropriate or desired.The presently disclosed subject matter is not limited to any particulartype of total or source of articles.

The application dashboard 202 may also include an articles section 210.The articles section 210 may include AI articles 118A-118N and/or humanarticle 122A. It is noted that the presently disclosed subject matter isnot limited to any particular number or type of each type of article.The articles rendered in the articles section 210 may be selectablelinks that, when selected, render the particle article within theapplication dashboard 202 or another interface. The AI articles118A-118N and/or the human article 122A may be rendered based on variousfactors. For example, the articles may be selected based on one or moresearches, user preferences, and the like.

The application dashboard 202 may also include a search input 212. Thesearch input 212 is configured to receive one or more textual or othertypes of inputs. The inputs received at the search input 212 are used bythe analyzer interface application 108 to perform a search on thearticles data store 116. Relevant results are returned and rendered inthe application dashboard 202. For example, the search results may berendered as articles in the articles section 210.

The application dashboard 202 may also include information ticker 214and article ticker 216. The information ticker 214 may include generalinformation such as information about the analyzer interface application108, the application dashboard 202, and the like. The article ticker 216may be summarized versions of articles, such as the articles in thearticles section 210.

FIG. 3 is a diagram showing the application dashboard 202 when using asearch function. In FIG. 3, upon the selection of a search function,such as the selected search control 206B, the application dashboard 202is modified to display various search tools. The application dashboard202 may render the search input 212 to receive one or more searchinputs.

The application dashboard 202 may also render search options 302. Thesearch options 302 may include or more selectable options for providinginput in addition to or in lieu of the input received in the searchinput 212. In the example illustrated, the selectable options include aBoolean operator 304 and search categories 306. The Boolean operator 304may be configured to receive one or more inputs and apply a particularBoolean function on the search terms. The search categories 306 includeone or more selectable and de-selectable sources of information.

In the example illustrated, the search categories 306 include sourcessuch as SEC regulatory news, trademark research news, and the like. Insome configurations, the AI articles 118 and/or the human articles 122may be categorized according to their subject matter. The subject mattermay be assigned as a particle source, such as a journal or publication.In the example illustrated, AI articles 118 relating to patents arecategorized within the journal, “Patent Research News.” In anotherexample, AI articles 118 relating to academic or commercial research maybe categorized within the journal, “News From Peer-Reviewed Research.”It is noted that the AI articles may be categorized in more than onejournal or source. The search categories 306, if unselected or selected,may be used to exclude or include articles from a particular source,respectively, when performing a search.

The application dashboard 202 may further include a data range input308. The date range input 308 may be configured to receive in input froma user. The input may be a desired date range within which the AIarticles 118 and/or the human articles 122 should be included in asearch, and, outside of which the AI articles 118 and/or the humanarticles 122 should not be included in a search.

The application dashboard 202 may also include a terms requirement input310. The terms requirement input 310 may be used to receive an inputregarding the percentage of terms entered in the search input 212 thatare required in the search results. For example, if the termsrequirement input 310 is at the 0% position, any term in the searchinput 212 is required to be found in the articles as a result of thesearch. If the terms requirement input 310 is moved towards the 100%position, such as the 78% position, 78 percent of the terms are requiredto be found in the articles as a result of the search. At the 100%position, all terms in the search input 212 are required to be found inthe articles as a result of the search.

The application dashboard 202 may additionally include an AI/humanrequirement input 312. The AI/human requirement input 312 may receive aninput to provide a desired ratio of AI to human articles provided as aresult of the search. For example, if at the AI position, all articlesreturned as a result of a search are within the AI articles 118. If atthe Human position, all articles returned as a result of a search arewithin the human articles 122. If at a position between the AI positionand the Human position, a ratio of articles in the AI articles 118 andthe human articles 122 is returned, the ratio depending on the positionin the AI/human requirement input 312.

The application dashboard 202 may also include a search database input314. The search database input 314 is configured as a selectable input.When selected, the search database input 314 invokes a search by theanalyzer interface application 108 using the terms entered in the searchinput 212 as well as the other input described in FIG. 3. It is noted,however, that other types of input may be used when performing a searchand are considered to be within the scope of the presently disclosedsubject matter.

FIG. 4 is a diagram showing the application dashboard 202 rendering anew discovery index 402. The new discovery index 402 may be a graphicalor numerical display showing a number of new discoveries for aparticular time period. As used herein, a new discovery means a person,place, or thing that has been discovered. In some examples, a newdiscovery includes discoveries resulting from scientific researchendeavors. In this example, the AI articles 118 may be analyzed todetermine if the content within one or more of the AI articles 118includes a new discovery.

A new discovery may be identified using various technologies. Forexample, a new discovery may be identified by terms or phrases commonlyfound in articles discussing new discoveries. In one example, the termsmay include, but are not limited to, “discovery,” “uncovered,” and“found.” The articles that include new discoveries may be categorizedaccording to the subject matter of the article and the number summed todetermine a total, which may be displayed in the new discovery index 402for a particular time period. The new discovery index 402 may includethe total number of all new discoveries or may include only newdiscoveries for particular subject matter, or various combinations.

FIG. 5 is a diagram showing the application dashboard 202 rendering atop stories and trending section 502. In some examples, the analyzerinterface application 108 may analyze the AI articles 118, the humanarticles 122, and/or the data sources 124 to determine topics or subjectmatter that is trending. As used herein, “trending” means that the topicor subject matter is present within a certain percentage of articles orits use is increasing at a certain rate.

The top stories and trending section 502 may include a recent headlinessection 504. The recent headlines section 504 may include articles abouta particular topic (e.g. engineering in the example illustrated in FIG.5) that have recently been generated or generated within a specifictime.

The top stories and trending section 502 may include a trending namessection 506. The trending names section 506 may list one or more namesof people or entities that are trending. The names may also include thenumber of articles in which the names are present. In the exampleillustrated in FIG. 5, the name BOOZ ALLEN is present in two (2)articles.

The top stories and trending section 502 may include a trending topicssection 508. The trending topics section 508 may be a graphical displayshowing the number of trending articles or total number of articles fora particular topic over a particular time period. In the exampleillustrated in FIG. 5, the total number of articles for the topicCHEMISTRY is rendered.

FIG. 6 is a diagram showing the application dashboard 202 rendering atop stories and trending section 502 having an articles by regionsection 602. In FIG. 6, the articles by region section 602 may be agraphical display showing the number of articles generated for aparticular topic (or number of articles generated overall) for aparticular location.

In the example illustrated in FIG. 6, the size of the circle, such ascircle 604, provides a visual display of the numbers of articles. InFIG. 6, the circle 604A is larger than the circle 604B. Thus, inrelative terms, the number of articles generated in the location of thecircle 604A is greater than the number of articles generated in thelocation of the circle 604B, both of which are greater than areas inwhich no circles are located.

FIG. 7 is a diagram showing the application dashboard 202 rendering acustom topics section 702. In some configurations, the custom topicssection 702 may be a configurable section that receives an input of whatto render in the custom topics section 702. The input may be one or moresubject matter areas. In another example, articles of different subjectmatter may be displayed for a particular topic.

In the example illustrated in FIG. 7, the topic of “entertainment” isselected in a topics selector 704 for the general subject matter areas“SEC Regulatory News,” “Patent Research News,” and “Trademark ResearchNews.” Articles of those general subject matters relating toentertainment may be rendered in the custom topics section 702. It isnoted that, in some configurations, the general subject matter areas maybe changed.

FIG. 8 is a diagram showing the application dashboard 202 by which ananalysis is conducted. In some configurations, it may be beneficial ordesired to analyze the AI articles 118 and/or the human articles 122. Ananalysis of the AI articles 118 and/or the human articles 122 may, amongother possible benefits, yield useful information about the AI articles118 and/or the human articles 122 other than the information containedwithin the articles themselves.

If, in FIG. 2, an input is received at the analyze control 206C (oranother input similarly configured), the application dashboard 202 maytake the configuration of FIG. 8. The application dashboard 202 hasrendered therein a data analyzer search input 802. The data analyzersearch input 802 includes a terms input 804 and a create analyzationinput 806. To commence an analysis, one or more terms may be received asinputs in the terms input 804 and the create analyzation input 806 isselected. Previously saved analyzations may be found in the savedanalyzations section 214.

Upon the receipt of the selection of the create analyzation input 806,the analyzer interface application 108 transmits a search query to theserver computer 104 with the terms entered into the terms input 804. Theserver computer 104 searches the articles data store 116 for one or morearticles relating to the search terms entered in the terms input 804.Once the create analyzation input 806 is selected and the search isperformed, the search results are returned to the analyzer interfaceapplication 108. In some examples, the search used may be termed a“faceted” search.

In some examples, a faceted search is a dynamic clustering of items orsearch results into categories that provide for the ability of users todrill (or explore in further detail) into search results (or even skipsearching entirely) by any value in any field. Each facet displayed canalso show a number of hits within a search that match a particularcategory. Users can then “drill down” by applying specific constraintsto the search results. A faceted search can also be called facetedbrowsing, faceted navigation, guided navigation and sometimes parametricsearch. The application dashboard 202 may be reconfigured in the mannerillustrated in FIG. 9.

FIG. 9 is a diagram showing the application dashboard 202 after a searchis performed by the server computer 104 in conjunction with an analysisfunction. The results of the searched are rendered in the resultssection 902. The application dashboard 202 may render a researchstatistics section 904. The research statistics section 904 may includeone or more metrics relating to the search results. For example, themetrics may include, but are not limited to, the total articles in thesearch results and the date range of the articles found.

In some configurations, the initial results in the results section 902may be a macro analysis step in which a broad range of search resultsare returned. In subsequent operations, it may be desirable to narrowthe results to a more focused set of resulted. In some examples, thebroad range of results may be narrowed using various filters.

One filter may be an exclude articles filter 906. The exclude articlesfilter 906 may receive as an input one or more terms that, if found inan article in the search results, eliminate the particular article. Forexample, a user may want to exclude all articles from a particularorganization. The user may input the organization's name into theexclude articles filter 906 and select a find matching articlesinterface 908. Once selected, the analyzer interface application 108will remove the articles having the organization's name from the searchresults.

The application dashboard 202 may also have rendered therein a researchcategories interface 910. The research categories interface 910 may beconfigured to receive as an input a selection of which subject matterareas or types of articles the search results should be found in. In oneexample, a user may deselect the “Patent Research News” article type,thus excluding articles associated with the “Patent Research News”article type from the search results.

The application dashboard 202 may further have rendered therein a daterange selection interface 912. The date range selection interface 912may receive as an input a date range of the articles or the data sources124, within which to include in the search results. Specific articleswithin the search results may be excluded using a selection interface,such as a selection interface 914. A remove selected articles interface916 may be selected to remove the articles selected using the selectioninterface 914. An input may be received at a save and continue interface918. Once an input is received at the save and continue interface 918,the various filters and other inputs are applied to the search results.

FIG. 10A is a diagram showing the application dashboard 202 after thevarious filters and other inputs are applied to search results. In someexamples, the processes illustrated in FIG. 10A may be termed a microanalysis step. The research statistics section 904 may be updated toindicate the results of applying the input from the configuration of theapplication dashboard 202 illustrated in FIG. 9.

To further narrow or focus the search results in the results section902, a filter results interface 920 may be provided. The filter resultsinterface 920 may be configured to receive one or more inputs of variousfilters to apply. The filters may be generated by terms or otherinformation contained within the search results. In the exampleillustrated in FIG. 10A, the filters may be “Institution/Company,”“Keywords,” “Regions,” “Topics,” and “Information Sources.” Each ofthese filters may be generated as a result of an analysis of the searchresults or may be predetermined. In one example, the filter entitled,“Regions,” includes the locations “Asia” and “Japan.” A selection ofeither one of these will exclude articles from those locations. Keywordsmay be generated using various technologies. In one example, an exampleof which is illustrated in FIG. 10B.

FIG. 10B is an example method 1000 for generating and applying one ormore keywords to associate with an article. At operation 1002, allnon-alphanumeric characters are removed from an article. At operation1004, the article is parsed into phrases to generate a searchable recordin a table. The process ends at operation 1008. The following is anillustration of the method 1000.

In the following example, an article comprising a sentence is received.The article for this example is “The cow jumped over the moon.” Thesentence is parsed into various phrases, such as the below, with eachphrase representing a searchable record in a table:

-   the-   the cow-   the cow jumped-   the cow jumped over-   cow-   cow jumped-   cow jumped over-   cow jumped over the-   jumped-   jumped over-   jumped over the-   jumped over the moon-   over-   over the-   over the moon

In the example illustrated above, the phrases were limited to fourterms, although the number of terms may be varied depending on theparticular application of method 1000. In another example, the followingsentence is provided: “There is a new drug development in AIDSvaccines.” Resulting searchable phrases may be “drug development,”“AIDS,” and “AIDS vaccine.” The parsed phrases may be applied askeywords to an article. The keywords may be grouped into particulartopics. For example, keywords associated with various cancers may begrouped under the topic, CANCERS. The names of the topics may be used tocreate chapter titles denoting a collection of articles under aparticular topic.

Returning to FIG. 10A, in another example, the “Topics” includes topicssuch as “Bacteria,” “Bacterial Infections,” and the like. A selection ofone or more of the topics will exclude articles with those topics. Onceselected, the remove selected articles interface 916 may be selected toremove the articles selected using the selection interface 914. An inputmay be received at the save and continue interface 918. Once an input isreceived at the save and continue interface 918, the various filters andother inputs are applied to the search results.

FIG. 11 is a diagram showing the application dashboard 202 after thevarious filters and other inputs are applied to search results in themicro analysis step. In FIG. 11, the remaining search results arepresented in the results section 902. The research statistics section904 may be updated to indicate the results of applying the input fromthe configuration of the application dashboard 202 illustrated in FIG.10A.

The search results may be analyzed to determine one or chapters toorganize the output of the search results. The initial chapters may berendered in the chapters section 922. The chapters may be generated fromvarious terms found in the search results or subject matter within whichthe search results belong, or other methods not disclosed herein. Forexample, the chapters may be based on keywords associated with thearticles searched. The keywords may be grouped into topics, with thename of the topic used as the chapter title.

The chapters may be edited or removed. Once an input is received at thesave and continue interface 918, the search results are organized intothe chapters rendered in the chapters section 922.

FIG. 12 is a diagram showing the application dashboard 202 after thechapters step is performed. The research statistics section 904 may beupdated to indicate the results of applying the input from theconfiguration of the application dashboard 202 illustrated in FIG. 11.

FIG. 12 illustrates an example of a visualization 926 that may beprovided for the search results. The visualization 926 may be agraphical representation of various aspects of the results from FIG. 11.In the example illustrated in FIG. 12, the visualization 926 is a bargraph illustrating the number of articles per subject matter, topic, orother category. The visualization 926 may be applicable to a particularchapter and may be changed to various chapters using the chapterselection interface 928. The particular topics within with the resultsare categorized may be rendered in a related topics section 930. Once aninput is received at the save and continue interface 918, the resultsare organized for download or transmission.

FIG. 13 is a diagram showing the application dashboard 202 havingrendered therein a download interface 932. A report may be generatedfrom the results of the operations illustrated above. The title andformat of the report may be selected or determined using the downloadinterface 932. A save and download interface 934 may receive an inputindicating that the report is to be downloaded for use.

FIG. 14 is a diagram showing the application dashboard 202 in aconfiguration to provide a visualization. The application dashboard 202has rendered therein a data visualizer interface 934. The datavisualizer interface 934 may be configured to receive an input of one ormore terms from which a visualization is to be generated. Upon a receiptof an input at a create visualization interface 936, the articlevisualizer 126 of FIG. 1 is invoked by the article analyzer 114. Thearticle analyzer 114 searches the articles data store 116 using theterms provided in the data visualizer interface 934. The results of thesearch are provided to the article visualizer 126. The articlevisualizer 126 receives the results and determines one or more metricsfrom the results, shown by way of example in FIG. 15.

FIG. 15 is a diagram showing the application dashboard 202 havingvisualizations provided by the article visualizer 126. Although notlimited to any particular type, visualizations may include, but are notlimited to, trends, top stories, pie charts, and regions. Thevisualizations may be graphical renditions of various aspects of thesearch and analysis performed by the article analyzer 114 and thearticle visualizer 126.

FIG. 16 is a flow diagram showing aspects of a method 1600 forgenerating an artificial intelligence article, in accordance with someembodiments. It should be understood that the operations of the methodsdisclosed herein are not necessarily presented in any particular orderand that performance of some or all of the operations in an alternativeorder(s) is possible and is contemplated. The operations have beenpresented in the demonstrated order for ease of description andillustration. Operations may be added, omitted, and/or performedsimultaneously, without departing from the scope of the appended claims.

It also should be understood that the illustrated methods can be endedat any time and need not be performed in its entirety. Some or alloperations of the methods, and/or substantially equivalent operations,can be performed by execution of computer-readable instructions includedon a computer-storage media, as defined herein. The term“computer-readable instructions,” and variants thereof, as used in thedescription and claims, is used expansively herein to include routines,applications, application modules, program modules, programs,components, data structures, algorithms, and the like. Computer-readableinstructions can be implemented on various system configurations,including single-processor or multiprocessor systems, minicomputers,mainframe computers, personal computers, hand-held computing devices,microprocessor-based, programmable consumer electronics, combinationsthereof, and the like.

Thus, it should be appreciated that the logical operations describedherein are implemented (1) as a sequence of computer implemented acts orprogram modules running on a computing system and/or (2) asinterconnected machine logic circuits or circuit modules within thecomputing system. The implementation is a matter of choice dependent onthe performance and other requirements of the computing system.Accordingly, the logical operations described herein are referred tovariously as states, operations, structural devices, acts, or modules.These operations, structural devices, acts, and modules may beimplemented in software, in firmware, in special purpose digital logic,and any combination thereof

The operations of the method 800 are described herein below as beingimplemented, at least in part, by the user device 102 and/or the servercomputer 104 (described above with regard to FIG. 1). One or more of theoperations of the method 800 may alternatively or additionally beimplemented, at least in part, by the similar components in a computerutilizing the computer architecture 1800 of FIG. 18 or a similarlyconfigured computer providing the operating environment 1900.

Now with reference to FIG. 16, the method 1600 begins and proceeds tooperation 1602, where data sources are accessed to receive rawinformation. The data sources may include information provided onvarious Internet websites, periodicals, regulatory and/or governmentalagencies, and the like. The presently disclosed subject matter is notlimited to any particular type of data source.

The method proceeds to operation 1604, where one or more filters areapplied to the raw information. The filters may be filters designed toremove words or text that are superfluous, indicate bias or opinion, orare otherwise undesired. The use of the filters on the raw informationresults in the generation of filtered information.

The method proceeds to operation 1606, where information is added to thefiltered information to connect concepts. The added information may beinformation (e.g. text) that, when added, to the filtered information,creates sentences that are akin to sentences desired or used by humans.The added information creates enhanced information.

The method proceeds to operation 1608, where the enhanced information isstored as an artificial intelligence article. The method 1600 thereafterends at operation 1610.

FIG. 17 is a flow diagram showing aspects of a method 1700 for analyzingdata using artificial intelligence articles, in accordance with someembodiments.

The method 1700 commences at operation 1702, where one or more searchterms are received by an article analyzer from an analyzer interfaceapplication. The search terms may include one or more subjects, keywords, and the like associated with a desired analysis.

The method 1700 proceeds to operation 1704, where the article analyzersearches the articles data store for articles relevant to the searchterms entered in operation 1702. The articles may be relevant if thearticles include the terms or include concepts similar to the termsreceived in operation 1702. In some examples, the articles data storemay include both artificial intelligence articles or human articles, orboth.

The method 1700 proceeds to operation 1706, where the search results arereturned to the analyzer interface application. The analyzer interfaceapplication may render the search results in a display. To focus ornarrow the search results, a macro analysis may be performed. In someexamples, the macro analysis may include one or more filters,exclusionary terms, date rangers, categories, and the like. The searchresults are modified based on the macro analysis inputs. In someexamples, the macro analysis may help narrow the search results.

The method 1700 proceeds to operation 1708, where micro analysis inputsare received. In some examples, the micro analysis may be used to removeunwanted content. In these examples, various concepts, topics,categories, information sources may be selected to be excluded. Thesearch results are further narrowed upon receipt of the micro analysisinput.

The method 1700 proceeds to operation 1710, where one or more chaptertitles are determined. The chapter titles may be determined based on thesubject matter of the search results, particular key words or phrases inthe search results, the data sources from which the articles weregenerated, or other technologies. The presently disclosed subject matteris not limited to any particular technology for generating chaptertitles. In some examples, the chapter titles may be removed or edited.

The method 1700 proceeds to operation 1712, where a report is generated.The report may include the articles from the search results separatedinto the chapter titles from operation 1710. The report may bedownloadable. The method 1700 thereafter ends at operation 1714.

FIG. 18 illustrates an illustrative computer architecture 1800 for adevice capable of executing the software and/or hardware componentsdescribed herein for an artificial intelligence article analysisinterface, in accordance with some embodiments. Thus, the computerarchitecture 1800 illustrated in FIG. 18 illustrates an architecture fora server computer, mobile phone, a PDA, a smart phone, a desktopcomputer, a netbook computer, a tablet computer, and/or a laptopcomputer. The computer architecture 1800 may be utilized to execute anyaspects of the software components presented herein.

The computer architecture 1800 illustrated in FIG. 18 includes a centralprocessing unit 1802 (“CPU”), a system memory 1804, including a randomaccess memory 1806 (“RAM”) and a read-only memory (“ROM”) 1808, and asystem bus 1810 that couples the memory 1804 to the CPU 1802. A basicinput/output system containing the basic routines that help to transferinformation between elements within the computer architecture 1800, suchas during startup, is stored in the ROM 1808. The computer architecture1800 further includes a mass storage device 1812 for storing the articleanalyzer 114, the artificial intelligence journalist 120, the articlesdata store 116, the AI articles 118, and the human articles 122.

The mass storage device 1812 is communicatively connected to the CPU1802 through a mass storage controller (not shown) connected to the bus1810. The mass storage device 1812 and its associated computer-readablemedia provide non-volatile storage for the computer architecture 1800.Although the description of computer-readable media contained hereinrefers to a mass storage device, such as a hard disk or CD-ROM drive, itshould be appreciated by those skilled in the art that computer-readablemedia can be any available computer storage media or communication mediathat can be accessed by the computer architecture 1800.

Communication media includes computer readable instructions, datastructures, program modules, or other data in a modulated data signalsuch as a carrier wave or other transport mechanism and includes anydelivery media. The term “modulated data signal” means a signal that hasone or more of its characteristics changed or set in a manner as toencode information in the signal. By way of example, and not limitation,communication media includes wired media such as a wired network ordirect-wired connection, and wireless media such as acoustic, RF,infrared and other wireless media. Combinations of the any of the aboveshould also be included within the scope of computer-readable media.

By way of example, and not limitation, computer storage media mayinclude volatile and non-volatile, removable and non-removable mediaimplemented in any method or technology for storage of information suchas computer-readable instructions, data structures, program modules orother data. For example, computer media includes, but is not limited to,RAM, ROM, EPROM, EEPROM, flash memory or other solid state memorytechnology, CD-ROM, digital versatile disks (“DVD”), HD-DVD, BLU-RAY, orother optical storage, magnetic cassettes, magnetic tape, magnetic diskstorage or other magnetic storage devices, or any other medium which canbe used to store the desired information and which can be accessed bythe computer architecture 1800. For purposes the claims, the phrase“computer storage medium” and variations thereof, does not includewaves, signals, and/or other transitory and/or intangible communicationmedia, per se.

According to various embodiments, the computer architecture 1800 mayoperate in a networked environment using logical connections to remotecomputers through a network such as the network 106. The computerarchitecture 1800 may connect to the network 106 through a networkinterface unit 1814 connected to the bus 1810. It should be appreciatedthat the network interface unit 1814 also may be utilized to connect toother types of networks and remote computer systems. The computerarchitecture 1800 also may include an input/output controller 1816 forreceiving and processing input from a number of other devices, includinga keyboard, mouse, or electronic stylus (not shown in FIG. 18).Similarly, the input/output controller 1816 may provide output to adisplay screen, a printer, or other type of output device (also notshown in FIG. 18).

It should be appreciated that the software components described hereinmay, when loaded into the CPU 1802 and executed, transform the CPU 1802and the overall computer architecture 1800 from a general-purposecomputing system into a special-purpose computing system customized tofacilitate the functionality presented herein. The CPU 1802 may beconstructed from any number of transistors or other discrete circuitelements, which may individually or collectively assume any number ofstates. More specifically, the CPU 1802 may operate as a finite-statemachine, in response to executable instructions contained within thesoftware modules disclosed herein. These computer-executableinstructions may transform the CPU 1802 by specifying how the CPU 1802transitions between states, thereby transforming the transistors orother discrete hardware elements constituting the CPU 1802.

Encoding the software modules presented herein also may transform thephysical structure of the computer-readable media presented herein. Thespecific transformation of physical structure may depend on variousfactors, in different implementations of this description. Examples ofsuch factors may include, but are not limited to, the technology used toimplement the computer-readable media, whether the computer-readablemedia is characterized as primary or secondary storage, and the like.For example, if the computer-readable media is implemented assemiconductor-based memory, the software disclosed herein may be encodedon the computer-readable media by transforming the physical state of thesemiconductor memory. For example, the software may transform the stateof transistors, capacitors, or other discrete circuit elementsconstituting the semiconductor memory. The software also may transformthe physical state of such components in order to store data thereupon.

As another example, the computer-readable media disclosed herein may beimplemented using magnetic or optical technology. In suchimplementations, the software presented herein may transform thephysical state of magnetic or optical media, when the software isencoded therein. These transformations may include altering the magneticcharacteristics of particular locations within given magnetic media.These transformations also may include altering the physical features orcharacteristics of particular locations within given optical media, tochange the optical characteristics of those locations. Othertransformations of physical media are possible without departing fromthe scope and spirit of the present description, with the foregoingexamples provided only to facilitate this discussion.

In light of the above, it should be appreciated that many types ofphysical transformations take place in the computer architecture 1800 inorder to store and execute the components presented herein. It alsoshould be appreciated that the computer architecture 1800 may includeother types of computing devices, including hand-held computers,embedded computer systems, personal digital assistants, and other typesof computing devices known to those skilled in the art. It is alsocontemplated that the computer architecture 1800 may not include all ofthe components shown in FIG. 18, may include other components that arenot explicitly shown in FIG. 18, or may utilize an architecturecompletely different than that shown in FIG. 18.

FIG. 19 illustrates an illustrative distributed computing environment1900 capable of executing the software and/or hardware componentsdescribed herein for an artificial intelligence article analysisinterface, in accordance with some embodiments. Thus, the distributedcomputing environment 1900 illustrated in FIG. 19 can be used to providethe functionality described herein with respect to the user device 102,the server computer 104, and/or the user device 114. The distributedcomputing environment 1900 thus may be utilized to execute any aspectsof the software components presented herein.

According to various implementations, the distributed computingenvironment 1900 includes a computing environment 1902 operating on, incommunication with, or as part of the network 106. The network 106 alsocan include various access networks. One or more client devices1906A-1906N (hereinafter referred to collectively and/or generically as“clients 1906”) can communicate with the computing environment 1902 viathe network 106 and/or other connections (not illustrated in FIG. 19).In the illustrated embodiment, the clients 1906 include a computingdevice 1906A such as a laptop computer, a desktop computer, or othercomputing device; a slate or tablet computing device (“tablet computingdevice”) 1906B; a mobile computing device 1906C such as a mobiletelephone, a smart phone, or other mobile computing device; a servercomputer 1906D; and/or other devices 1906N. It should be understood thatany number of clients 1906 can communicate with the computingenvironment 1902. It should be understood that the illustrated clients1906 and computing architectures illustrated and described herein areillustrative, and should not be construed as being limited in any way.

In the illustrated embodiment, the computing environment 1902 includesapplication servers 1908, data storage 1910, and one or more networkinterfaces 1912. According to various implementations, the functionalityof the application servers 1908 can be provided by one or more servercomputers that are executing as part of, or in communication with, thenetwork 1904. The application servers 1908 can host various services,virtual machines, portals, and/or other resources. In the illustratedembodiment, the application servers 1908 host one or more virtualmachines 1914 for hosting applications or other functionality. Accordingto various implementations, the virtual machines 1914 host one or moreapplications and/or software modules for providing the functionalitydescribed herein for use in an artificial intelligence article analysisinterface. It should be understood that this embodiment is illustrative,and should not be construed as being limiting in any way. Theapplication servers 1908 also host or provide access to one or more Webportals, link pages, Web sites, and/or other information (“Web portals”)1916.

As shown in FIG. 19, the application servers 1908 also can host otherservices, applications, portals, and/or other resources (“otherresources”) 1924. It thus can be appreciated that the computingenvironment 1902 can provide integration of the concepts andtechnologies disclosed herein provided herein for use in an artificialintelligence article analysis interface. It should be understood thatthese embodiments are illustrative, and should not be construed as beinglimiting in any way.

As mentioned above, the computing environment 1902 can include the datastorage 1910. According to various implementations, the functionality ofthe data storage 1910 is provided by one or more databases operating on,or in communication with, the network 1904. The functionality of thedata storage 1910 also can be provided by one or more server computersconfigured to host data for the computing environment 1902. The datastorage 1910 can include, host, or provide one or more real or virtualdata stores 1926A-1926N (hereinafter referred to collectively and/orgenerically as “data stores 1926”). The data stores 1926 are configuredto host data used or created by the application servers 1908 and/orother data.

The computing environment 1902 can communicate with, or be accessed by,the network interfaces 1912. The network interfaces 1912 can includevarious types of network hardware and software for supportingcommunications between two or more computing devices including, but notlimited to, the clients 1906 and the application servers 1908. It shouldbe appreciated that the network interfaces 1912 also may be utilized toconnect to other types of networks and/or computer systems.

It should be understood that the distributed computing environment 1900described herein can provide any aspects of the software elementsdescribed herein with any number of virtual computing resources and/orother distributed computing functionality that can be configured toexecute any aspects of the software components disclosed herein.According to various implementations of the concepts and technologiesdisclosed herein, the distributed computing environment 1900 providesthe software functionality described herein as a service to the clients1906. It should be understood that the clients 1906 can include real orvirtual machines including, but not limited to, server computers, webservers, personal computers, mobile computing devices, smart phones,and/or other devices. As such, various embodiments of the concepts andtechnologies disclosed herein enable any device configured to access thedistributed computing environment 1900 to utilize the functionalitydescribed herein for use in an artificial intelligence article analysisinterface.

Based on the foregoing, it should be appreciated that technologies foruse in an artificial intelligence article analysis interface have beendisclosed herein. Although the subject matter presented herein has beendescribed in language specific to computer structural features,methodological and transformative acts, specific computing machinery,and computer readable media, it is to be understood that the inventiondefined in the appended claims is not necessarily limited to thespecific features, acts, or media described herein. Rather, the specificfeatures, acts and mediums are disclosed as example forms ofimplementing the claims.

The subject matter described above is provided by way of illustrationonly and should not be construed as limiting. Various modifications andchanges may be made to the subject matter described herein withoutfollowing the example embodiments and applications illustrated anddescribed, and without departing from the true spirit and scope of thepresent invention, which is set forth in the following claims.

1. A computer, comprising: a processor; and a computer-readable mediumin communication with the processor, the computer-readable mediumcomprising computer-executable instructions that, when executed by theprocessor, cause the computer to search, using a search query, anarticles data store, the articles data store comprising a plurality ofartificial intelligence articles, to generate a plurality of searchresults, perform an analysis on the plurality of search results, andgenerate a report based on the analysis.
 2. The computer of claim 1,wherein the articles data store further comprises a plurality of humanarticles.
 3. The computer of claim 1, wherein the computer-executableinstructions to perform an analysis comprises computer-executableinstructions to perform a macro analysis of the search results using oneor more filters to filter out one or more results of the plurality ofsearch results.
 4. The computer of claim 3, wherein thecomputer-executable instructions to perform an analysis furthercomprises computer-executable instructions to perform a micro analysisusing one or more keywords to remove unwanted content.
 5. The computerof claim 1, further comprising computer-executable instructions thatcause the computer to determine one or more chapter titles for thereport.
 6. The computer of claim 5, further comprisingcomputer-executable instructions that cause the computer to separate thereport into the one or more chapter titles.
 7. The computer of claim 5,wherein the one or more chapter titles are one or more topics associatedwith the plurality of search results.
 8. The computer of claim 7,wherein a topic of the one or more topics comprises a grouping ofkeywords associated with a particular subject, whereby the subject isthe topic.
 9. The computer of claim 8, wherein the grouping of keywordsis generated by: removing non-alphabetic and non-numeric characters froman article, parsing the article into phrases to generate a searchablerecord of the phrases in a table; and sorting the table.
 10. Thecomputer of claim 1, further comprising computer-executable instructionsthat cause the computer to receive an AI/human requirement inputidentifying a ratio of artificial intelligence articles to humanarticles provided in the plurality of search results.
 11. The computerof claim 1, further comprising computer-executable instructions thatcause the computer to: determine a plurality of metrics relating to theplurality of search results; and output a visualization using theplurality of metrics.
 12. A method, comprising: generating a pluralityof artificial intelligence articles, wherein each of the plurality ofartificial intelligence articles are generated by receiving rawinformation, applying a filter to the raw information to generatefiltered information, adding information to the filtered information tocreate enhanced information, and storing the enhanced information as anarticle of the plurality of artificial intelligence articles; receivinga search query; receiving a terms requirement input indicating apercentage of terms in the search query that are required in a pluralityof search results; searching an articles data store using the searchquery to generate the plurality of search results, the articles datastore comprising the plurality of artificial intelligence articles;performing a macro analysis on the plurality of search results using oneor more filters to filter out one or more results of the plurality ofsearch results; performing a micro analysis on the plurality of searchresults using one or more keywords to remove unwanted content; andgenerating a report based on the analysis.
 13. The method of claim 12,wherein the articles data store further comprises a plurality of humanarticles.
 14. The method of claim 12, further comprising determining oneor more chapter titles for the report.
 15. The method of claim 14,further comprising separating the report into the one or more chaptertitles.
 16. The method of claim 15, wherein the one or more chaptertitles are one or more topics associated with the plurality of searchresults.
 17. The method of claim 16, wherein a topic of the one or moretopics comprises a grouping of keywords associated with a particularsubject, whereby the subject is the topic. 18-19. (canceled)
 20. Acomputer-readable medium having computer-executable instructionsthereupon that, when executed by a computer, cause the computer to:execute an article analyzer that determines a plurality of metricsrelating to a plurality of search results received from a search of anarticles data store that comprises a plurality of artificialintelligence articles, performs a macro analysis on the plurality ofsearch results using one or more filters to filter out one or moreresults of the plurality of search results, performs a micro analysis onthe plurality of search results using one or more keywords to removeunwanted content, and generates a report based on the analysis; andexecute an article visualizer that outputs a visualization using theplurality of metrics.
 21. The computer-readable medium of claim 20,further comprising computer-executable instructions thereupon that, whenexecuted by a computer, cause the computer to receive a search query.22. The computer-readable medium of claim 20, wherein the articles datastore further comprises a plurality of human articles.